The state of the art is believed to: be represented by the following publications:    S-H. Chiang, A. Arpaci-Dusseau, and M. K. Vernon, “The impact of more accurate requested runtimes on production job scheduling performance”. In Job Scheduling Strategies for Parallel Processing, pp. 103-127, Springer Verlag, 2002. LNCS 2537.    A. B. Downey, “Predicting queue times on space-sharing parallel computers”. In 11th Intl. Parallel Processing Symp., pp. 209-218, April 1997.    R. Gibbons, “A historical application profiler for use by parallel schedulers”. In Job Scheduling Strategies for Parallel Processing, pp. 58-77, Springer Verlag, 1997. LNCS 1291.    B. G. Lawson and E. Smirni, “Multiple-queue backfilling scheduling with priorities and reservations for parallel systems”. In Job Scheduling Strategies for Parallel Processing, pp. 72-87, Springer Verlag, July 2002. LNCS 2537.    A. W. Mu'alem and D. G. Feitelson, “Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling”. IEEE Trans. Parallel & Distributed Syst. 12(6), pp. 529-543, June 2001.    W. Smith, I. Foster, and V. Taylor, “Predicting application run times using historical information”. In Job Scheduling Strategies for Parallel Processing, pp. 122-142, Springer Verlag, 1998. LNCS 1459.    W. Smith, V. Taylor, and I. Foster, “Using run-time predictions to estimate queue wait times and improve scheduler performance”. In Job Scheduling Strategies for Parallel Processing, pp. 202-219, Springer Verlag, 1999. LNCS 1659.    Shonali Krishnaswamy et al, “Estimating Computation Times of Data-Intensive Applications”, IEEE Distributed Systems online, April 2004 (Vol. 5, No. 4).
The disclosures of all publications mentioned in the specification, and of the publications cited therein directly or indirectly, are hereby incorporated by reference.